import flair
import torch
from flair.models import TARSSequenceTagger2
from flair.data import Sentence
from flair.datasets import MIT_MOVIE_NER_COMPLEX
flair.set_seed(3)
tagger = TARSSequenceTagger2.load("resources/v3/conll_3-cryptic/final-model.pt")
label_name_map = {
"Character_Name":"Character Name"
}
print(label_name_map)
corpus = MIT_MOVIE_NER_COMPLEX(tag_to_bioes=None, tag_to_bio2="ner", label_name_map=label_name_map)
corpus = corpus.downsample(0.1)
tag_type = "ner"
tag_dictionary = corpus.make_label_dictionary(tag_type)
tagger.add_and_switch_to_new_task("zeroshot-conll_3-cryptic-to-moviecomplex", tag_dictionary=tag_dictionary, tag_type=tag_type)
result, eval_loss = tagger.evaluate(corpus.test)
print(result.main_score)
print(result.log_header)
print(result.log_line)
print(result.detailed_results)
print(eval_loss)
# evaluation
sentences = [
Sentence("The Parlament of the United Kingdom is discussing a variety of topics."),
